{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.6.10"
    },
    "colab": {
      "name": "FinRL_multiple_stock_trading.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "uijiWgkuh1jB",
        "MRiOtrywfAo1",
        "_gDkU-j-fCmZ",
        "3Zpv4S0-fDBv"
      ],
      "toc_visible": true,
      "include_colab_link": true
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AI4Finance-LLC/FinRL-Library/blob/master/FinRL_multiple_stock_trading.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gXaoZs2lh1hi"
      },
      "source": [
        "# Deep Reinforcement Learning for Stock Trading from Scratch: Multiple Stock Trading\n",
        "\n",
        "Tutorials to use OpenAI DRL to trade multiple stocks in one Jupyter Notebook | Presented at NeurIPS 2020: Deep RL Workshop\n",
        "\n",
        "* This blog is based on our paper: FinRL: A Deep Reinforcement Learning Library for Automated Stock Trading in Quantitative Finance, presented at NeurIPS 2020: Deep RL Workshop.\n",
        "* Check out medium blog for detailed explanations: https://towardsdatascience.com/finrl-for-quantitative-finance-tutorial-for-multiple-stock-trading-7b00763b7530\n",
        "* Please report any issues to our Github: https://github.com/AI4Finance-LLC/FinRL-Library/issues\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lGunVt8oLCVS"
      },
      "source": [
        "# Content"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HOzAKQ-SLGX6"
      },
      "source": [
        "* [1. Problem Definition](#0)\n",
        "* [2. Getting Started - Load Python packages](#1)\n",
        "    * [2.1. Install Packages](#1.1)    \n",
        "    * [2.2. Check Additional Packages](#1.2)\n",
        "    * [2.3. Import Packages](#1.3)\n",
        "    * [2.4. Create Folders](#1.4)\n",
        "* [3. Download Data](#2)\n",
        "* [4. Preprocess Data](#3)        \n",
        "    * [4.1. Technical Indicators](#3.1)\n",
        "    * [4.2. Perform Feature Engineering](#3.2)\n",
        "* [5.Build Environment](#4)  \n",
        "    * [5.1. Training & Trade Data Split](#4.1)\n",
        "    * [5.2. User-defined Environment](#4.2)   \n",
        "    * [5.3. Initialize Environment](#4.3)    \n",
        "* [6.Implement DRL Algorithms](#5)  \n",
        "* [7.Backtesting Performance](#6)  \n",
        "    * [7.1. BackTestStats](#6.1)\n",
        "    * [7.2. BackTestPlot](#6.2)   \n",
        "    * [7.3. Baseline Stats](#6.3)   \n",
        "    * [7.3. Compare to Stock Market Index](#6.4)             "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sApkDlD9LIZv"
      },
      "source": [
        "<a id='0'></a>\n",
        "# Part 1. Problem Definition"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HjLD2TZSLKZ-"
      },
      "source": [
        "This problem is to design an automated trading solution for single stock trading. We model the stock trading process as a Markov Decision Process (MDP). We then formulate our trading goal as a maximization problem.\n",
        "\n",
        "The algorithm is trained using Deep Reinforcement Learning (DRL) algorithms and the components of the reinforcement learning environment are:\n",
        "\n",
        "\n",
        "* Action: The action space describes the allowed actions that the agent interacts with the\n",
        "environment. Normally, a ∈ A includes three actions: a ∈ {−1, 0, 1}, where −1, 0, 1 represent\n",
        "selling, holding, and buying one stock. Also, an action can be carried upon multiple shares. We use\n",
        "an action space {−k, ..., −1, 0, 1, ..., k}, where k denotes the number of shares. For example, \"Buy\n",
        "10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or −10, respectively\n",
        "\n",
        "* Reward function: r(s, a, s′) is the incentive mechanism for an agent to learn a better action. The change of the portfolio value when action a is taken at state s and arriving at new state s',  i.e., r(s, a, s′) = v′ − v, where v′ and v represent the portfolio\n",
        "values at state s′ and s, respectively\n",
        "\n",
        "* State: The state space describes the observations that the agent receives from the environment. Just as a human trader needs to analyze various information before executing a trade, so\n",
        "our trading agent observes many different features to better learn in an interactive environment.\n",
        "\n",
        "* Environment: Dow 30 consituents\n",
        "\n",
        "\n",
        "The data of the single stock that we will be using for this case study is obtained from Yahoo Finance API. The data contains Open-High-Low-Close price and volume.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ffsre789LY08"
      },
      "source": [
        "<a id='1'></a>\n",
        "# Part 2. Getting Started- Load Python Packages"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uy5_PTmOh1hj"
      },
      "source": [
        "<a id='1.1'></a>\n",
        "## 2.1. Install all the packages through FinRL library\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mPT0ipYE28wL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d1f16fed-61e5-4b5f-b6c8-60d63478d433"
      },
      "source": [
        "## install finrl library\n",
        "!pip install git+https://github.com/AI4Finance-LLC/FinRL-Library.git"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/AI4Finance-LLC/FinRL-Library.git\n",
            "  Cloning https://github.com/AI4Finance-LLC/FinRL-Library.git to /tmp/pip-req-build-zfgnkmv8\n",
            "  Running command git clone -q https://github.com/AI4Finance-LLC/FinRL-Library.git /tmp/pip-req-build-zfgnkmv8\n",
            "Requirement already satisfied: numpy<1.19.0,>=1.16.0 in /usr/local/lib/python3.6/dist-packages (from finrl==0.0.1) (1.18.5)\n",
            "Requirement already satisfied: pandas==1.1.4 in /usr/local/lib/python3.6/dist-packages (from finrl==0.0.1) (1.1.4)\n",
            "Collecting stockstats\n",
            "  Downloading https://files.pythonhosted.org/packages/32/41/d3828c5bc0a262cb3112a4024108a3b019c183fa3b3078bff34bf25abf91/stockstats-0.3.2-py2.py3-none-any.whl\n",
            "Collecting yfinance\n",
            "  Downloading https://files.pythonhosted.org/packages/7a/e8/b9d7104d3a4bf39924799067592d9e59119fcfc900a425a12e80a3123ec8/yfinance-0.1.55.tar.gz\n",
            "Collecting scikit-learn==0.21.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b7/6c/ec121123c671d980c6969dfc69d0f09e1d7f88d80d373f511e61d773b85c/scikit_learn-0.21.0-cp36-cp36m-manylinux1_x86_64.whl (6.6MB)\n",
            "\u001b[K     |████████████████████████████████| 6.6MB 6.8MB/s \n",
            "\u001b[?25hCollecting gym==0.15.3\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d2/88/a7186ffe1f33570ad3b8cd635996e5a3e3e155736e180ae6a2ad5e826a60/gym-0.15.3.tar.gz (1.6MB)\n",
            "\u001b[K     |████████████████████████████████| 1.6MB 45.8MB/s \n",
            "\u001b[?25hCollecting stable-baselines[mpi]\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b0/48/d428b79bd4360727925f9fe34afeea7a9da381da3dc8748df834a349ad1d/stable_baselines-2.10.1-py3-none-any.whl (240kB)\n",
            "\u001b[K     |████████████████████████████████| 245kB 44.6MB/s \n",
            "\u001b[?25hCollecting tensorflow==1.15.4\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8e/64/7a19837dd54d3f53b1ce5ae346ab401dde9678e8f233220317000bfdb3e2/tensorflow-1.15.4-cp36-cp36m-manylinux2010_x86_64.whl (110.5MB)\n",
            "\u001b[K     |████████████████████████████████| 110.5MB 103kB/s \n",
            "\u001b[?25hCollecting joblib==0.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/b8/a6/d1a816b89aa1e9e96bcb298eb1ee1854f21662ebc6d55ffa3d7b3b50122b/joblib-0.15.1-py3-none-any.whl (298kB)\n",
            "\u001b[K     |████████████████████████████████| 307kB 45.9MB/s \n",
            "\u001b[?25hCollecting matplotlib==3.2.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/93/4b/52da6b1523d5139d04e02d9e26ceda6146b48f2a4e5d2abfdf1c7bac8c40/matplotlib-3.2.1-cp36-cp36m-manylinux1_x86_64.whl (12.4MB)\n",
            "\u001b[K     |████████████████████████████████| 12.4MB 46.2MB/s \n",
            "\u001b[?25hCollecting pytest<6.0.0,>=5.3.2\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/9f/f3/0a83558da436a081344aa6c8b85ea5b5f05071214106036ce341b7769b0b/pytest-5.4.3-py3-none-any.whl (248kB)\n",
            "\u001b[K     |████████████████████████████████| 256kB 40.3MB/s \n",
            "\u001b[?25hCollecting setuptools<42.0.0,>=41.4.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/d9/de/554b6310ac87c5b921bc45634b07b11394fe63bc4cb5176f5240addf18ab/setuptools-41.6.0-py2.py3-none-any.whl (582kB)\n",
            "\u001b[K     |████████████████████████████████| 583kB 45.9MB/s \n",
            "\u001b[?25hCollecting wheel<0.34.0,>=0.33.6\n",
            "  Downloading https://files.pythonhosted.org/packages/00/83/b4a77d044e78ad1a45610eb88f745be2fd2c6d658f9798a15e384b7d57c9/wheel-0.33.6-py2.py3-none-any.whl\n",
            "Collecting pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2\n",
            "  Cloning https://github.com/quantopian/pyfolio.git to /tmp/pip-install-l3uchb8n/pyfolio\n",
            "  Running command git clone -q https://github.com/quantopian/pyfolio.git /tmp/pip-install-l3uchb8n/pyfolio\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas==1.1.4->finrl==0.0.1) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.6/dist-packages (from pandas==1.1.4->finrl==0.0.1) (2.8.1)\n",
            "Collecting int-date>=0.1.7\n",
            "  Downloading https://files.pythonhosted.org/packages/43/27/31803df15173ab341fe7548c14154b54227dfd8f630daa09a1c6e7db52f7/int_date-0.1.8-py2.py3-none-any.whl\n",
            "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from yfinance->finrl==0.0.1) (2.23.0)\n",
            "Requirement already satisfied: multitasking>=0.0.7 in /usr/local/lib/python3.6/dist-packages (from yfinance->finrl==0.0.1) (0.0.9)\n",
            "Collecting lxml>=4.5.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/bd/78/56a7c88a57d0d14945472535d0df9fb4bbad7d34ede658ec7961635c790e/lxml-4.6.2-cp36-cp36m-manylinux1_x86_64.whl (5.5MB)\n",
            "\u001b[K     |████████████████████████████████| 5.5MB 37.2MB/s \n",
            "\u001b[?25hRequirement already satisfied: scipy>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn==0.21.0->finrl==0.0.1) (1.4.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from gym==0.15.3->finrl==0.0.1) (1.15.0)\n",
            "Collecting pyglet<=1.3.2,>=1.2.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1c/fc/dad5eaaab68f0c21e2f906a94ddb98175662cc5a654eee404d59554ce0fa/pyglet-1.3.2-py2.py3-none-any.whl (1.0MB)\n",
            "\u001b[K     |████████████████████████████████| 1.0MB 42.9MB/s \n",
            "\u001b[?25hCollecting cloudpickle~=1.2.0\n",
            "  Downloading https://files.pythonhosted.org/packages/c1/49/334e279caa3231255725c8e860fa93e72083567625573421db8875846c14/cloudpickle-1.2.2-py2.py3-none-any.whl\n",
            "Requirement already satisfied: opencv-python in /usr/local/lib/python3.6/dist-packages (from stable-baselines[mpi]->finrl==0.0.1) (4.1.2.30)\n",
            "Collecting mpi4py; extra == \"mpi\"\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ec/8f/bbd8de5ba566dd77e408d8136e2bab7fdf2b97ce06cab830ba8b50a2f588/mpi4py-3.0.3.tar.gz (1.4MB)\n",
            "\u001b[K     |████████████████████████████████| 1.4MB 41.6MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.12.1)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.1.0)\n",
            "Collecting keras-applications>=1.0.8\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/71/e3/19762fdfc62877ae9102edf6342d71b28fbfd9dea3d2f96a882ce099b03f/Keras_Applications-1.0.8-py3-none-any.whl (50kB)\n",
            "\u001b[K     |████████████████████████████████| 51kB 6.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-pasta>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (0.2.0)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (0.8.1)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.1.2)\n",
            "Collecting gast==0.2.2\n",
            "  Downloading https://files.pythonhosted.org/packages/4e/35/11749bf99b2d4e3cceb4d55ca22590b0d7c2c62b9de38ac4a4a7f4687421/gast-0.2.2.tar.gz\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (3.12.4)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (3.3.0)\n",
            "Collecting tensorboard<1.16.0,>=1.15.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/1e/e9/d3d747a97f7188f48aa5eda486907f3b345cd409f0a0850468ba867db246/tensorboard-1.15.0-py3-none-any.whl (3.8MB)\n",
            "\u001b[K     |████████████████████████████████| 3.8MB 41.2MB/s \n",
            "\u001b[?25hCollecting tensorflow-estimator==1.15.1\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/de/62/2ee9cd74c9fa2fa450877847ba560b260f5d0fb70ee0595203082dafcc9d/tensorflow_estimator-1.15.1-py2.py3-none-any.whl (503kB)\n",
            "\u001b[K     |████████████████████████████████| 512kB 38.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (1.33.2)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.15.4->finrl==0.0.1) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.2.1->finrl==0.0.1) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.2.1->finrl==0.0.1) (1.3.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib==3.2.1->finrl==0.0.1) (0.10.0)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (8.6.0)\n",
            "Collecting pluggy<1.0,>=0.12\n",
            "  Downloading https://files.pythonhosted.org/packages/a0/28/85c7aa31b80d150b772fbe4a229487bc6644da9ccb7e427dd8cc60cb8a62/pluggy-0.13.1-py2.py3-none-any.whl\n",
            "Requirement already satisfied: importlib-metadata>=0.12; python_version < \"3.8\" in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (2.0.0)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (20.3.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (0.2.5)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (1.9.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from pytest<6.0.0,>=5.3.2->finrl==0.0.1) (20.4)\n",
            "Requirement already satisfied: ipython>=3.2.3 in /usr/local/lib/python3.6/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (5.5.0)\n",
            "Requirement already satisfied: seaborn>=0.7.1 in /usr/local/lib/python3.6/dist-packages (from pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.11.0)\n",
            "Collecting empyrical>=0.5.0\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/74/43/1b997c21411c6ab7c96dc034e160198272c7a785aeea7654c9bcf98bec83/empyrical-0.5.5.tar.gz (52kB)\n",
            "\u001b[K     |████████████████████████████████| 61kB 7.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (2020.11.8)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->yfinance->finrl==0.0.1) (2.10)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.3.2,>=1.2.0->gym==0.15.3->finrl==0.0.1) (0.16.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.8->tensorflow==1.15.4->finrl==0.0.1) (2.10.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4->finrl==0.0.1) (3.3.3)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.16.0,>=1.15.0->tensorflow==1.15.4->finrl==0.0.1) (1.0.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/dist-packages (from importlib-metadata>=0.12; python_version < \"3.8\"->pytest<6.0.0,>=5.3.2->finrl==0.0.1) (3.4.0)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.8.1)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (4.8.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (4.4.2)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (4.3.3)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (1.0.18)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (2.6.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.7.5)\n",
            "Requirement already satisfied: pandas-datareader>=0.2 in /usr/local/lib/python3.6/dist-packages (from empyrical>=0.5.0->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.9.0)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.6.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython>=3.2.3->pyfolio@ git+https://github.com/quantopian/pyfolio.git#egg=pyfolio-0.9.2->finrl==0.0.1) (0.2.0)\n",
            "Building wheels for collected packages: finrl, yfinance, gym, pyfolio, mpi4py, gast, empyrical\n",
            "  Building wheel for finrl (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for finrl: filename=finrl-0.0.1-cp36-none-any.whl size=24307 sha256=83673260396a21ce53db94879fd36aee62b93c885de942b57c2ca99546984dd9\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ecwf7_on/wheels/9c/19/bf/c644def96612df1ad42c94d5304966797eaa3221dffc5efe0b\n",
            "  Building wheel for yfinance (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for yfinance: filename=yfinance-0.1.55-py2.py3-none-any.whl size=22618 sha256=d315128d3fe2811ac1a535eec5b63942aece15092dd868e7db94eddbbbb5004d\n",
            "  Stored in directory: /root/.cache/pip/wheels/04/98/cc/2702a4242d60bdc14f48b4557c427ded1fe92aedf257d4565c\n",
            "  Building wheel for gym (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gym: filename=gym-0.15.3-cp36-none-any.whl size=1644971 sha256=56ccf3fd6aa6c4d25a99625e2d48440ff72935d2af0f9fdfdfd99410035c8f41\n",
            "  Stored in directory: /root/.cache/pip/wheels/8a/71/10/30f9b16332ecfd6318ac290445c696fe809bcbe40a05f9a799\n",
            "  Building wheel for pyfolio (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyfolio: filename=pyfolio-0.9.2+75.g4b901f6-cp36-none-any.whl size=75764 sha256=515ac10348df020d4df2fdd647c4c4b2e5cd85215f4004330cdc9807ac36c270\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-ecwf7_on/wheels/43/ce/d9/6752fb6e03205408773235435205a0519d2c608a94f1976e56\n",
            "  Building wheel for mpi4py (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for mpi4py: filename=mpi4py-3.0.3-cp36-cp36m-linux_x86_64.whl size=2074486 sha256=bc707c6ee010dcfd4e89b1d9274d305d0239da173b3e2177b2af54e94a506546\n",
            "  Stored in directory: /root/.cache/pip/wheels/18/e0/86/2b713dd512199096012ceca61429e12b960888de59818871d6\n",
            "  Building wheel for gast (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for gast: filename=gast-0.2.2-cp36-none-any.whl size=7542 sha256=17eba6dd12db8f6cff9528e9deba8e359440d02dfe7c42531c506c5798b49e1b\n",
            "  Stored in directory: /root/.cache/pip/wheels/5c/2e/7e/a1d4d4fcebe6c381f378ce7743a3ced3699feb89bcfbdadadd\n",
            "  Building wheel for empyrical (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for empyrical: filename=empyrical-0.5.5-cp36-none-any.whl size=39763 sha256=5b55a3ea266ccf3f3d1f5c658d6314fb5c41688dcddcb035cd0b15457329f518\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/b2/c8/6769d8444d2f2e608fae2641833110668d0ffd1abeb2e9f3fc\n",
            "Successfully built finrl yfinance gym pyfolio mpi4py gast empyrical\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement cloudpickle==1.3, but you'll have cloudpickle 1.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: tensorflow-probability 0.11.0 has requirement gast>=0.3.2, but you'll have gast 0.2.2 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: datascience 0.10.6 has requirement folium==0.2.1, but you'll have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Installing collected packages: int-date, stockstats, lxml, yfinance, joblib, scikit-learn, pyglet, cloudpickle, gym, matplotlib, mpi4py, stable-baselines, wheel, keras-applications, gast, setuptools, tensorboard, tensorflow-estimator, tensorflow, pluggy, pytest, empyrical, pyfolio, finrl\n",
            "  Found existing installation: lxml 4.2.6\n",
            "    Uninstalling lxml-4.2.6:\n",
            "      Successfully uninstalled lxml-4.2.6\n",
            "  Found existing installation: joblib 0.17.0\n",
            "    Uninstalling joblib-0.17.0:\n",
            "      Successfully uninstalled joblib-0.17.0\n",
            "  Found existing installation: scikit-learn 0.22.2.post1\n",
            "    Uninstalling scikit-learn-0.22.2.post1:\n",
            "      Successfully uninstalled scikit-learn-0.22.2.post1\n",
            "  Found existing installation: pyglet 1.5.0\n",
            "    Uninstalling pyglet-1.5.0:\n",
            "      Successfully uninstalled pyglet-1.5.0\n",
            "  Found existing installation: cloudpickle 1.3.0\n",
            "    Uninstalling cloudpickle-1.3.0:\n",
            "      Successfully uninstalled cloudpickle-1.3.0\n",
            "  Found existing installation: gym 0.17.3\n",
            "    Uninstalling gym-0.17.3:\n",
            "      Successfully uninstalled gym-0.17.3\n",
            "  Found existing installation: matplotlib 3.2.2\n",
            "    Uninstalling matplotlib-3.2.2:\n",
            "      Successfully uninstalled matplotlib-3.2.2\n",
            "  Found existing installation: wheel 0.35.1\n",
            "    Uninstalling wheel-0.35.1:\n",
            "      Successfully uninstalled wheel-0.35.1\n",
            "  Found existing installation: gast 0.3.3\n",
            "    Uninstalling gast-0.3.3:\n",
            "      Successfully uninstalled gast-0.3.3\n",
            "  Found existing installation: setuptools 50.3.2\n",
            "    Uninstalling setuptools-50.3.2:\n",
            "      Successfully uninstalled setuptools-50.3.2\n",
            "  Found existing installation: tensorboard 2.3.0\n",
            "    Uninstalling tensorboard-2.3.0:\n",
            "      Successfully uninstalled tensorboard-2.3.0\n",
            "  Found existing installation: tensorflow-estimator 2.3.0\n",
            "    Uninstalling tensorflow-estimator-2.3.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.3.0\n",
            "  Found existing installation: tensorflow 2.3.0\n",
            "    Uninstalling tensorflow-2.3.0:\n",
            "      Successfully uninstalled tensorflow-2.3.0\n",
            "  Found existing installation: pluggy 0.7.1\n",
            "    Uninstalling pluggy-0.7.1:\n",
            "      Successfully uninstalled pluggy-0.7.1\n",
            "  Found existing installation: pytest 3.6.4\n",
            "    Uninstalling pytest-3.6.4:\n",
            "      Successfully uninstalled pytest-3.6.4\n",
            "Successfully installed cloudpickle-1.2.2 empyrical-0.5.5 finrl-0.0.1 gast-0.2.2 gym-0.15.3 int-date-0.1.8 joblib-0.15.1 keras-applications-1.0.8 lxml-4.6.2 matplotlib-3.2.1 mpi4py-3.0.3 pluggy-0.13.1 pyfolio-0.9.2+75.g4b901f6 pyglet-1.3.2 pytest-5.4.3 scikit-learn-0.21.0 setuptools-41.6.0 stable-baselines-2.10.1 stockstats-0.3.2 tensorboard-1.15.0 tensorflow-1.15.4 tensorflow-estimator-1.15.1 wheel-0.33.6 yfinance-0.1.55\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "matplotlib",
                  "mpl_toolkits",
                  "pkg_resources"
                ]
              }
            }
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "osBHhVysOEzi"
      },
      "source": [
        "\n",
        "<a id='1.2'></a>\n",
        "## 2.2. Check if the additional packages needed are present, if not install them. \n",
        "* Yahoo Finance API\n",
        "* pandas\n",
        "* numpy\n",
        "* matplotlib\n",
        "* stockstats\n",
        "* OpenAI gym\n",
        "* stable-baselines\n",
        "* tensorflow\n",
        "* pyfolio"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oirvDTJ0h1hk"
      },
      "source": [
        "import pkg_resources\n",
        "import pip\n",
        "installedPackages = {pkg.key for pkg in pkg_resources.working_set}\n",
        "required = {'yfinance', 'pandas','numpy', 'matplotlib', 'stockstats','stable-baselines','gym','tensorflow','pyfolio'}\n",
        "missing = required - installedPackages\n",
        "if missing:\n",
        "    !pip install yfinance\n",
        "    !pip install pandas\n",
        "    !pip install numpy\n",
        "    !pip install matplotlib\n",
        "    !pip install stockstats\n",
        "    !pip install gym\n",
        "    !pip install stable-baselines[mpi]\n",
        "    !pip install tensorflow==1.15.4\n",
        "    !pip install git+https://github.com/quantopian/pyfolio\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nGv01K8Sh1hn"
      },
      "source": [
        "<a id='1.3'></a>\n",
        "## 2.3. Import Packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lPqeTTwoh1hn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "877392c5-c55b-4d05-cb89-649531176bd6"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "matplotlib.use('Agg')\n",
        "import datetime\n",
        "\n",
        "from finrl.config import config\n",
        "from finrl.marketdata.yahoodownloader import YahooDownloader\n",
        "from finrl.preprocessing.preprocessors import FeatureEngineer\n",
        "from finrl.preprocessing.data import data_split\n",
        "from finrl.env.environment import EnvSetup\n",
        "from finrl.env.EnvMultipleStock_train import StockEnvTrain\n",
        "from finrl.env.EnvMultipleStock_trade import StockEnvTrade\n",
        "from finrl.model.models import DRLAgent\n",
        "from finrl.trade.backtest import BackTestStats, BaselineStats, BackTestPlot\n",
        "\n"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:\n",
            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
            "For more information, please see:\n",
            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
            "  * https://github.com/tensorflow/addons\n",
            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
            "If you depend on functionality not listed there, please file an issue.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/pos.py:27: UserWarning: Module \"zipline.assets\" not found; multipliers will not be applied to position notionals.\n",
            "  'Module \"zipline.assets\" not found; multipliers will not be applied'\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T2owTj985RW4"
      },
      "source": [
        "<a id='1.4'></a>\n",
        "## 2.4. Create Folders"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9A8CN5R5PuZ"
      },
      "source": [
        "import os\n",
        "if not os.path.exists(\"./\" + config.DATA_SAVE_DIR):\n",
        "    os.makedirs(\"./\" + config.DATA_SAVE_DIR)\n",
        "if not os.path.exists(\"./\" + config.TRAINED_MODEL_DIR):\n",
        "    os.makedirs(\"./\" + config.TRAINED_MODEL_DIR)\n",
        "if not os.path.exists(\"./\" + config.TENSORBOARD_LOG_DIR):\n",
        "    os.makedirs(\"./\" + config.TENSORBOARD_LOG_DIR)\n",
        "if not os.path.exists(\"./\" + config.RESULTS_DIR):\n",
        "    os.makedirs(\"./\" + config.RESULTS_DIR)"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A289rQWMh1hq"
      },
      "source": [
        "<a id='2'></a>\n",
        "# Part 3. Download Data\n",
        "Yahoo Finance is a website that provides stock data, financial news, financial reports, etc. All the data provided by Yahoo Finance is free.\n",
        "* FinRL uses a class **YahooDownloader** to fetch data from Yahoo Finance API\n",
        "* Call Limit: Using the Public API (without authentication), you are limited to 2,000 requests per hour per IP (or up to a total of 48,000 requests a day).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NPeQ7iS-LoMm"
      },
      "source": [
        "\n",
        "\n",
        "-----\n",
        "class YahooDownloader:\n",
        "    Provides methods for retrieving daily stock data from\n",
        "    Yahoo Finance API\n",
        "\n",
        "    Attributes\n",
        "    ----------\n",
        "        start_date : str\n",
        "            start date of the data (modified from config.py)\n",
        "        end_date : str\n",
        "            end date of the data (modified from config.py)\n",
        "        ticker_list : list\n",
        "            a list of stock tickers (modified from config.py)\n",
        "\n",
        "    Methods\n",
        "    -------\n",
        "    fetch_data()\n",
        "        Fetches data from yahoo API\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "h3XJnvrbLp-C",
        "outputId": "72caa213-b381-41f8-c882-b7b3383ad1c0"
      },
      "source": [
        "# from config.py start_date is a string\n",
        "config.START_DATE"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2009-01-01'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "FUnY8WEfLq3C",
        "outputId": "e12e5fe8-9f95-45c9-b33b-2a55a3ee871e"
      },
      "source": [
        "# from config.py end_date is a string\n",
        "config.END_DATE"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2020-09-30'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yCKm4om-s9kE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2dc811a-959f-47dc-cc57-1c6b102a35f6"
      },
      "source": [
        "df = YahooDownloader(start_date = config.START_DATE,\n",
        "                     end_date = config.END_DATE,\n",
        "                     ticker_list = config.DOW_30_TICKER).fetch_data()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (88680, 7)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CV3HrZHLh1hy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "481174fb-c44c-4b5b-9966-50019397e9df"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(88680, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "id": "4hYkeaPiICHS",
        "outputId": "a1f54b38-e5de-4cc2-8c80-cbf2f281185d"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high        low      close       volume   tic\n",
              "0  2009-01-02   3.067143   3.251429   3.041429   2.773207  746015200.0  AAPL\n",
              "1  2009-01-02  18.570000  19.520000  18.400000  15.800624   10955700.0   AXP\n",
              "2  2009-01-02  42.799999  45.560001  42.779999  33.680935    7010200.0    BA\n",
              "3  2009-01-02  44.910000  46.980000  44.709999  32.514400    7117200.0   CAT\n",
              "4  2009-01-02  16.410000  17.000000  16.250000  12.786087   40980600.0  CSCO"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uqC6c40Zh1iH"
      },
      "source": [
        "# Part 4: Preprocess Data\n",
        "Data preprocessing is a crucial step for training a high quality machine learning model. We need to check for missing data and do feature engineering in order to convert the data into a model-ready state.\n",
        "* Add technical indicators. In practical trading, various information needs to be taken into account, for example the historical stock prices, current holding shares, technical indicators, etc. In this article, we demonstrate two trend-following technical indicators: MACD and RSI.\n",
        "* Add turbulence index. Risk-aversion reflects whether an investor will choose to preserve the capital. It also influences one's trading strategy when facing different market volatility level. To control the risk in a worst-case scenario, such as financial crisis of 2007–2008, FinRL employs the financial turbulence index that measures extreme asset price fluctuation."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Le342Hc1h1iI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "43fc438e-430e-434a-f63d-1b6f89ca98bb"
      },
      "source": [
        "df = FeatureEngineer(df.copy(),\n",
        "                    use_technical_indicator=True,\n",
        "                    tech_indicator_list = config.TECHNICAL_INDICATORS_LIST,\n",
        "                    use_turbulence=True,\n",
        "                    user_defined_feature = False).preprocess_data()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Successfully added technical indicators\n",
            "Successfully added turbulence index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "grvhGJJII3Xn",
        "outputId": "dee5c9cd-ecd6-4d20-e5f9-6c92bd272bc1"
      },
      "source": [
        "df.sort_values(['date','tic'],ignore_index=True).head(10)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>74.230003</td>\n",
              "      <td>77.300003</td>\n",
              "      <td>73.580002</td>\n",
              "      <td>48.043262</td>\n",
              "      <td>13695900.0</td>\n",
              "      <td>CVX</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>21.605234</td>\n",
              "      <td>22.060680</td>\n",
              "      <td>20.993229</td>\n",
              "      <td>14.527276</td>\n",
              "      <td>13251000.0</td>\n",
              "      <td>DD</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>22.760000</td>\n",
              "      <td>24.030001</td>\n",
              "      <td>22.500000</td>\n",
              "      <td>20.597496</td>\n",
              "      <td>9796600.0</td>\n",
              "      <td>DIS</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>84.019997</td>\n",
              "      <td>87.620003</td>\n",
              "      <td>82.190002</td>\n",
              "      <td>72.844467</td>\n",
              "      <td>14088500.0</td>\n",
              "      <td>GS</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>23.070000</td>\n",
              "      <td>24.190001</td>\n",
              "      <td>22.959999</td>\n",
              "      <td>18.007103</td>\n",
              "      <td>14902500.0</td>\n",
              "      <td>HD</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...     cci_30  dx_30  turbulence\n",
              "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
              "1  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
              "2  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
              "3  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
              "4  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
              "5  2009-01-02  74.230003  77.300003  ...  66.666667  100.0         0.0\n",
              "6  2009-01-02  21.605234  22.060680  ...  66.666667  100.0         0.0\n",
              "7  2009-01-02  22.760000  24.030001  ...  66.666667  100.0         0.0\n",
              "8  2009-01-02  84.019997  87.620003  ...  66.666667  100.0         0.0\n",
              "9  2009-01-02  23.070000  24.190001  ...  66.666667  100.0         0.0\n",
              "\n",
              "[10 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-QsYaY0Dh1iw"
      },
      "source": [
        "<a id='4'></a>\n",
        "# Part 5. Design Environment\n",
        "Considering the stochastic and interactive nature of the automated stock trading tasks, a financial task is modeled as a **Markov Decision Process (MDP)** problem. The training process involves observing stock price change, taking an action and reward's calculation to have the agent adjusting its strategy accordingly. By interacting with the environment, the trading agent will derive a trading strategy with the maximized rewards as time proceeds.\n",
        "\n",
        "Our trading environments, based on OpenAI Gym framework, simulate live stock markets with real market data according to the principle of time-driven simulation.\n",
        "\n",
        "The action space describes the allowed actions that the agent interacts with the environment. Normally, action a includes three actions: {-1, 0, 1}, where -1, 0, 1 represent selling, holding, and buying one share. Also, an action can be carried upon multiple shares. We use an action space {-k,…,-1, 0, 1, …, k}, where k denotes the number of shares to buy and -k denotes the number of shares to sell. For example, \"Buy 10 shares of AAPL\" or \"Sell 10 shares of AAPL\" are 10 or -10, respectively. The continuous action space needs to be normalized to [-1, 1], since the policy is defined on a Gaussian distribution, which needs to be normalized and symmetric."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5TOhcryx44bb"
      },
      "source": [
        "## Training data split: 2009-01-01 to 2018-12-31\n",
        "## Trade data split: 2019-01-01 to 2020-09-30"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0qaVGjLtgbI"
      },
      "source": [
        "train = data_split(df, config.START_DATE,config.START_TRADE_DATE)\n",
        "trade = data_split(df,config.START_TRADE_DATE,config.END_DATE)\n"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p52zNCOhTtLR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "e1d2fede-61be-4c02-d04c-cf0bde221a56"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>3.067143</td>\n",
              "      <td>3.251429</td>\n",
              "      <td>3.041429</td>\n",
              "      <td>2.773207</td>\n",
              "      <td>746015200.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>18.570000</td>\n",
              "      <td>19.520000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>15.800624</td>\n",
              "      <td>10955700.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>42.799999</td>\n",
              "      <td>45.560001</td>\n",
              "      <td>42.779999</td>\n",
              "      <td>33.680935</td>\n",
              "      <td>7010200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>44.910000</td>\n",
              "      <td>46.980000</td>\n",
              "      <td>44.709999</td>\n",
              "      <td>32.514400</td>\n",
              "      <td>7117200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-02</td>\n",
              "      <td>16.410000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>16.250000</td>\n",
              "      <td>12.786087</td>\n",
              "      <td>40980600.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>0.0</td>\n",
              "      <td>100.0</td>\n",
              "      <td>66.666667</td>\n",
              "      <td>100.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date       open       high  ...     cci_30  dx_30  turbulence\n",
              "0  2009-01-02   3.067143   3.251429  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  18.570000  19.520000  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  42.799999  45.560001  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  44.910000  46.980000  ...  66.666667  100.0         0.0\n",
              "0  2009-01-02  16.410000  17.000000  ...  66.666667  100.0         0.0\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k9zU9YaTTvFq",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "0e67fc7d-b61c-489c-88bb-100783b56209"
      },
      "source": [
        "trade.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>open</th>\n",
              "      <th>high</th>\n",
              "      <th>low</th>\n",
              "      <th>close</th>\n",
              "      <th>volume</th>\n",
              "      <th>tic</th>\n",
              "      <th>macd</th>\n",
              "      <th>rsi_30</th>\n",
              "      <th>cci_30</th>\n",
              "      <th>dx_30</th>\n",
              "      <th>turbulence</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>38.722500</td>\n",
              "      <td>39.712502</td>\n",
              "      <td>38.557499</td>\n",
              "      <td>38.249401</td>\n",
              "      <td>148158800.0</td>\n",
              "      <td>AAPL</td>\n",
              "      <td>-2.003499</td>\n",
              "      <td>37.867367</td>\n",
              "      <td>-91.588048</td>\n",
              "      <td>42.250808</td>\n",
              "      <td>119.8791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>93.910004</td>\n",
              "      <td>96.269997</td>\n",
              "      <td>93.769997</td>\n",
              "      <td>92.643311</td>\n",
              "      <td>4175400.0</td>\n",
              "      <td>AXP</td>\n",
              "      <td>-3.426008</td>\n",
              "      <td>41.204982</td>\n",
              "      <td>-97.742269</td>\n",
              "      <td>26.709417</td>\n",
              "      <td>119.8791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>316.190002</td>\n",
              "      <td>323.950012</td>\n",
              "      <td>313.709991</td>\n",
              "      <td>314.645142</td>\n",
              "      <td>3292200.0</td>\n",
              "      <td>BA</td>\n",
              "      <td>-5.550592</td>\n",
              "      <td>47.010000</td>\n",
              "      <td>-21.712382</td>\n",
              "      <td>13.611972</td>\n",
              "      <td>119.8791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>124.029999</td>\n",
              "      <td>127.879997</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>119.302582</td>\n",
              "      <td>4783200.0</td>\n",
              "      <td>CAT</td>\n",
              "      <td>-0.686759</td>\n",
              "      <td>48.229089</td>\n",
              "      <td>-5.091209</td>\n",
              "      <td>0.873482</td>\n",
              "      <td>119.8791</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>42.279999</td>\n",
              "      <td>43.200001</td>\n",
              "      <td>42.209999</td>\n",
              "      <td>40.382099</td>\n",
              "      <td>23833500.0</td>\n",
              "      <td>CSCO</td>\n",
              "      <td>-0.960061</td>\n",
              "      <td>44.872557</td>\n",
              "      <td>-87.496850</td>\n",
              "      <td>29.529377</td>\n",
              "      <td>119.8791</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date        open        high  ...     cci_30      dx_30  turbulence\n",
              "0  2019-01-02   38.722500   39.712502  ... -91.588048  42.250808    119.8791\n",
              "0  2019-01-02   93.910004   96.269997  ... -97.742269  26.709417    119.8791\n",
              "0  2019-01-02  316.190002  323.950012  ... -21.712382  13.611972    119.8791\n",
              "0  2019-01-02  124.029999  127.879997  ...  -5.091209   0.873482    119.8791\n",
              "0  2019-01-02   42.279999   43.200001  ... -87.496850  29.529377    119.8791\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q2zqII8rMIqn"
      },
      "source": [
        "stock_dimension = len(train.tic.unique())\n",
        "state_space = 1 + 2*stock_dimension + len(config.TECHNICAL_INDICATORS_LIST)*stock_dimension"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AWyp84Ltto19"
      },
      "source": [
        "env_setup = EnvSetup(stock_dim = stock_dimension,\n",
        "                        state_space = state_space,\n",
        "                        hmax = 100,\n",
        "                        initial_amount = 1000000,\n",
        "                        transaction_cost_pct = 0.001)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "64EoqOrQjiVf"
      },
      "source": [
        "## Environment for Training\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwSvvPjutpqS"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockEnvTrain)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4QrezXm3jwu4"
      },
      "source": [
        "## Environment for Trading\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5_2x5PwMtqoN"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\n",
        "                                         env_class = StockEnvTrade) "
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HMNR5nHjh1iz"
      },
      "source": [
        "<a id='5'></a>\n",
        "# Part 6: Implement DRL Algorithms\n",
        "* The implementation of the DRL algorithms are based on **OpenAI Baselines** and **Stable Baselines**. Stable Baselines is a fork of OpenAI Baselines, with a major structural refactoring, and code cleanups.\n",
        "* FinRL library includes fine-tuned standard DRL algorithms, such as DQN, DDPG,\n",
        "Multi-Agent DDPG, PPO, SAC, A2C and TD3. We also allow users to\n",
        "design their own DRL algorithms by adapting these DRL algorithms."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "364PsqckttcQ"
      },
      "source": [
        "agent = DRLAgent(env = env_train)"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YDmqOyF9h1iz"
      },
      "source": [
        "### Model Training: 5 models, A2C DDPG, PPO, TD3, SAC\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uijiWgkuh1jB"
      },
      "source": [
        "### Model 1: A2C\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tDLXWsDctyuz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "8d885762-c74f-4bb0-ae8f-1dbece1937c5"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "a2c_params_tuning = {'n_steps':5, \n",
        "\t\t\t  'ent_coef':0.005, \n",
        "\t\t\t  'learning_rate':0.0002,\n",
        "\t\t\t  'verbose':0,\n",
        "\t\t\t  'timesteps':150000}\n",
        "model_a2c = agent.train_A2C(model_name = \"A2C_{}\".format(now), model_params = a2c_params_tuning)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:191: The name tf.ConfigProto is deprecated. Please use tf.compat.v1.ConfigProto instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:200: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/policies.py:116: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/input.py:25: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/policies.py:561: flatten (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.flatten instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/layers/core.py:332: Layer.apply (from tensorflow.python.keras.engine.base_layer) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_layers.py:123: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/distributions.py:418: The name tf.random_normal is deprecated. Please use tf.random.normal instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:160: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:449: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/ops/clip_ops.py:301: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:184: The name tf.train.RMSPropOptimizer is deprecated. Please use tf.compat.v1.train.RMSPropOptimizer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow_core/python/training/rmsprop.py:119: calling Ones.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:194: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py:196: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/base_class.py:1169: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.\n",
            "\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2687745.218248674\n",
            "total_reward:1687745.2182486742\n",
            "total_cost:  266522.6134965125\n",
            "total_trades:  73481\n",
            "Sharpe:  0.7375052943758552\n",
            "=================================\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:502: The name tf.Summary is deprecated. Please use tf.compat.v1.Summary instead.\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-18-47601f041898>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m                           \u001b[0;34m'verbose'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \t\t\t  'timesteps':150000}\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mmodel_a2c\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0magent\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_A2C\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"A2C_{}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnow\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0ma2c_params_tuning\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/finrl/model/models.py\u001b[0m in \u001b[0;36mtrain_A2C\u001b[0;34m(self, model_name, model_params)\u001b[0m\n\u001b[1;32m     51\u001b[0m                     \u001b[0mtensorboard_log\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"{config.TENSORBOARD_LOG_DIR}/{model_name}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m                     )\n\u001b[0;32m---> 53\u001b[0;31m         \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlearn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtotal_timesteps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel_params\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'timesteps'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_log_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"A2C_run\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     54\u001b[0m         \u001b[0mend\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py\u001b[0m in \u001b[0;36mlearn\u001b[0;34m(self, total_timesteps, callback, log_interval, tb_log_name, reset_num_timesteps)\u001b[0m\n\u001b[1;32m    261\u001b[0m                 \u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_rollout_start\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    262\u001b[0m                 \u001b[0;31m# true_reward is the reward without discount\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 263\u001b[0;31m                 \u001b[0mrollout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrunner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    264\u001b[0m                 \u001b[0;31m# unpack\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    265\u001b[0m                 \u001b[0mobs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrewards\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmasks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mactions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mep_infos\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrue_reward\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrollout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/common/runners.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, callback)\u001b[0m\n\u001b[1;32m     46\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcallback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     47\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 48\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_run\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     49\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     50\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mabstractmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/a2c/a2c.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    365\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    366\u001b[0m                 \u001b[0;31m# Abort training early\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 367\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate_locals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    368\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    369\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontinue_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/stable_baselines/common/callbacks.py\u001b[0m in \u001b[0;36mupdate_locals\u001b[0;34m(self, locals_)\u001b[0m\n\u001b[1;32m     59\u001b[0m         \u001b[0;34m:\u001b[0m\u001b[0mparam\u001b[0m\u001b[0;31m \u001b[0m\u001b[0;31m`\u001b[0m\u001b[0mlocals_\u001b[0m\u001b[0;31m`\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mDict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mlocal\u001b[0m \u001b[0mvariables\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     60\u001b[0m         \"\"\"\n\u001b[0;32m---> 61\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlocals\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlocals_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     62\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     63\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MRiOtrywfAo1"
      },
      "source": [
        "### Model 2: DDPG"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tCDa78rqfO_a",
        "outputId": "4f68dc4d-6034-4db8-9466-700c9108c379"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "ddpg_params_tuning = {\n",
        "                     'batch_size': 128,\n",
        "\t\t\t               'buffer_size':100000, \n",
        "\t\t\t               'verbose':0,\n",
        "\t\t\t               'timesteps':50000}\n",
        "model_ddpg = agent.train_DDPG(model_name = \"DDPG_{}\".format(now), model_params = ddpg_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/policies.py:136: dense (from tensorflow.python.layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/ddpg.py:94: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ddpg/ddpg.py:444: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/common/tf_util.py:432: The name tf.get_default_session is deprecated. Please use tf.compat.v1.get_default_session instead.\n",
            "\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3876639.240769919\n",
            "total_reward:2876639.240769919\n",
            "total_cost:  9104.342746239017\n",
            "total_trades:  41738\n",
            "Sharpe:  1.0054552314500205\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:7374327.234833065\n",
            "total_reward:6374327.234833065\n",
            "total_cost:  7039.457090340651\n",
            "total_trades:  35912\n",
            "Sharpe:  1.0833990891351075\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5248428.140151841\n",
            "total_reward:4248428.140151841\n",
            "total_cost:  8429.359631345911\n",
            "total_trades:  35402\n",
            "Sharpe:  1.03103504525587\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4718093.052233357\n",
            "total_reward:3718093.052233357\n",
            "total_cost:  10634.70695874195\n",
            "total_trades:  40388\n",
            "Sharpe:  0.9591292955030087\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5706787.595736295\n",
            "total_reward:4706787.595736295\n",
            "total_cost:  5088.324251377151\n",
            "total_trades:  38449\n",
            "Sharpe:  1.046294941645763\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6114764.380374785\n",
            "total_reward:5114764.380374785\n",
            "total_cost:  14639.86218725568\n",
            "total_trades:  38118\n",
            "Sharpe:  1.0978102756473607\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4845266.699600824\n",
            "total_reward:3845266.699600824\n",
            "total_cost:  11057.790948118627\n",
            "total_trades:  40872\n",
            "Sharpe:  0.9254254994857597\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5751061.008915898\n",
            "total_reward:4751061.008915898\n",
            "total_cost:  12918.164268064022\n",
            "total_trades:  41476\n",
            "Sharpe:  1.0214272741940498\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:7274435.433939911\n",
            "total_reward:6274435.433939911\n",
            "total_cost:  5971.728876357337\n",
            "total_trades:  43841\n",
            "Sharpe:  1.0950865802250083\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4615901.514024919\n",
            "total_reward:3615901.514024919\n",
            "total_cost:  10510.182272158909\n",
            "total_trades:  42251\n",
            "Sharpe:  0.9167348239963505\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5304047.0198513195\n",
            "total_reward:4304047.0198513195\n",
            "total_cost:  9589.782395890712\n",
            "total_trades:  41870\n",
            "Sharpe:  0.9245465984803718\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5625461.008441765\n",
            "total_reward:4625461.008441765\n",
            "total_cost:  17341.482320000207\n",
            "total_trades:  42376\n",
            "Sharpe:  0.9406268261325159\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5376035.759855201\n",
            "total_reward:4376035.759855201\n",
            "total_cost:  10974.242491519171\n",
            "total_trades:  42631\n",
            "Sharpe:  0.9597042150192086\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5676323.370232296\n",
            "total_reward:4676323.370232296\n",
            "total_cost:  12975.520036184482\n",
            "total_trades:  37313\n",
            "Sharpe:  0.9099386726375742\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:5396300.48576089\n",
            "total_reward:4396300.48576089\n",
            "total_cost:  16881.80250696935\n",
            "total_trades:  42983\n",
            "Sharpe:  0.9337169196345171\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3497906.5797796934\n",
            "total_reward:2497906.5797796934\n",
            "total_cost:  13012.68433630999\n",
            "total_trades:  43816\n",
            "Sharpe:  0.7534791881929837\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:1971547.527567742\n",
            "total_reward:971547.5275677419\n",
            "total_cost:  8164.89994731881\n",
            "total_trades:  39717\n",
            "Sharpe:  0.4443095749118807\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6060737.70392393\n",
            "total_reward:5060737.70392393\n",
            "total_cost:  21260.86078310216\n",
            "total_trades:  41248\n",
            "Sharpe:  1.0596003374109402\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:6188313.433283304\n",
            "total_reward:5188313.433283304\n",
            "total_cost:  16321.73783524186\n",
            "total_trades:  41381\n",
            "Sharpe:  0.9911460122517576\n",
            "=================================\n",
            "Training time (DDPG):  4.71319918235143  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_gDkU-j-fCmZ"
      },
      "source": [
        "### Model 3: PPO"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AXo1pwzHfUci",
        "outputId": "e47fa2af-5e4d-4cea-f5e6-552c9387bddb"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "ppo_params_tuning = {'n_steps':128, \n",
        "                     'nminibatches': 4,\n",
        "\t\t\t               'ent_coef':0.005, \n",
        "\t\t\t               'learning_rate':0.00025,\n",
        "\t\t\t              'verbose':0,\n",
        "\t\t\t              'timesteps':50000}\n",
        "model_ppo = agent.train_PPO(model_name = \"PPO_{}\".format(now), model_params = ppo_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:198: The name tf.trainable_variables is deprecated. Please use tf.compat.v1.trainable_variables instead.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/stable_baselines/ppo2/ppo2.py:206: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
            "\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3048741.3939381745\n",
            "total_reward:2048741.3939381745\n",
            "total_cost:  245568.3302282693\n",
            "total_trades:  72462\n",
            "Sharpe:  0.9202481544831457\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3293240.652184626\n",
            "total_reward:2293240.652184626\n",
            "total_cost:  208401.75301929898\n",
            "total_trades:  69573\n",
            "Sharpe:  0.9548892552085766\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3770063.9598072213\n",
            "total_reward:2770063.9598072213\n",
            "total_cost:  163858.87472231584\n",
            "total_trades:  66022\n",
            "Sharpe:  1.0100154114628663\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3533338.6618839577\n",
            "total_reward:2533338.6618839577\n",
            "total_cost:  140710.91081377305\n",
            "total_trades:  63468\n",
            "Sharpe:  0.8658687183317452\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3624214.190819685\n",
            "total_reward:2624214.190819685\n",
            "total_cost:  154313.0847744043\n",
            "total_trades:  65276\n",
            "Sharpe:  0.8993471111207018\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3202571.663253775\n",
            "total_reward:2202571.663253775\n",
            "total_cost:  178124.36502179946\n",
            "total_trades:  67223\n",
            "Sharpe:  0.7764722688501976\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3071845.7908768407\n",
            "total_reward:2071845.7908768407\n",
            "total_cost:  174568.22859443238\n",
            "total_trades:  66552\n",
            "Sharpe:  0.9040177248410571\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2759716.805770842\n",
            "total_reward:1759716.805770842\n",
            "total_cost:  141406.5384208243\n",
            "total_trades:  63691\n",
            "Sharpe:  0.7943240340782525\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3013214.0920152925\n",
            "total_reward:2013214.0920152925\n",
            "total_cost:  171745.69359744896\n",
            "total_trades:  66307\n",
            "Sharpe:  0.7831482945763572\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2696810.6705320706\n",
            "total_reward:1696810.6705320706\n",
            "total_cost:  159408.83254381173\n",
            "total_trades:  65545\n",
            "Sharpe:  0.7148325207248967\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3092140.8783237417\n",
            "total_reward:2092140.8783237417\n",
            "total_cost:  155293.8358448932\n",
            "total_trades:  64122\n",
            "Sharpe:  0.7113783235078796\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3264985.1486329967\n",
            "total_reward:2264985.1486329967\n",
            "total_cost:  169097.90147489944\n",
            "total_trades:  65095\n",
            "Sharpe:  0.8611881140368268\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3106629.5796447564\n",
            "total_reward:2106629.5796447564\n",
            "total_cost:  156059.88216477341\n",
            "total_trades:  64735\n",
            "Sharpe:  0.8337757600671555\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3101765.563581535\n",
            "total_reward:2101765.563581535\n",
            "total_cost:  141704.11478872094\n",
            "total_trades:  63020\n",
            "Sharpe:  0.7868378968332507\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3870851.8138185116\n",
            "total_reward:2870851.8138185116\n",
            "total_cost:  139163.3939960446\n",
            "total_trades:  62628\n",
            "Sharpe:  1.0398952470182161\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3971468.1489379085\n",
            "total_reward:2971468.1489379085\n",
            "total_cost:  96543.31543344291\n",
            "total_trades:  56808\n",
            "Sharpe:  1.074727317652792\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3862850.6640476706\n",
            "total_reward:2862850.6640476706\n",
            "total_cost:  68077.1690398825\n",
            "total_trades:  52321\n",
            "Sharpe:  1.0505756197667369\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4750937.677483732\n",
            "total_reward:3750937.677483732\n",
            "total_cost:  77272.76184075551\n",
            "total_trades:  54465\n",
            "Sharpe:  1.1094458223439796\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4404566.248673428\n",
            "total_reward:3404566.248673428\n",
            "total_cost:  78227.07486048219\n",
            "total_trades:  53813\n",
            "Sharpe:  1.0844266786058263\n",
            "=================================\n",
            "Training time (PPO):  2.3141635179519655  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3Zpv4S0-fDBv"
      },
      "source": [
        "### Model 4: TD3"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0S5WIHJffWdP",
        "outputId": "750ba07b-d0b1-483c-c654-95fefe5562a4"
      },
      "source": [
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "td3_params_tuning = {\n",
        "                     'batch_size': 128,\n",
        "\t\t\t               'buffer_size':200000, \n",
        "                     'learning_rate': 0.0002,\n",
        "\t\t\t               'verbose':0,\n",
        "\t\t\t               'timesteps':50000}\n",
        "model_td3 = agent.train_TD3(model_name = \"TD3_{}\".format(now), model_params = td3_params_tuning)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2662114.6849694927\n",
            "total_reward:1662114.6849694927\n",
            "total_cost:  5154.707305372425\n",
            "total_trades:  35274\n",
            "Sharpe:  0.6952087010828358\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2988142.5784285073\n",
            "total_reward:1988142.5784285073\n",
            "total_cost:  1632.59511942303\n",
            "total_trades:  40163\n",
            "Sharpe:  0.7289450168804903\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2883484.519447818\n",
            "total_reward:1883484.519447818\n",
            "total_cost:  1214.8873761913726\n",
            "total_trades:  37755\n",
            "Sharpe:  0.7095756761323406\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2873489.4430507906\n",
            "total_reward:1873489.4430507906\n",
            "total_cost:  1209.6024044447397\n",
            "total_trades:  37753\n",
            "Sharpe:  0.7123425504445975\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2915279.9369537304\n",
            "total_reward:1915279.9369537304\n",
            "total_cost:  1228.91289347425\n",
            "total_trades:  37752\n",
            "Sharpe:  0.7163200645078105\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:2895154.510926656\n",
            "total_reward:1895154.510926656\n",
            "total_cost:  1263.9253499270492\n",
            "total_trades:  35674\n",
            "Sharpe:  0.7150207708720131\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3019681.6740699634\n",
            "total_reward:2019681.6740699634\n",
            "total_cost:  999.0000961172603\n",
            "total_trades:  35210\n",
            "Sharpe:  0.751148686794223\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3013520.945701581\n",
            "total_reward:2013520.945701581\n",
            "total_cost:  999.0009760471949\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7469440146690564\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3042938.2329919036\n",
            "total_reward:2042938.2329919036\n",
            "total_cost:  998.9997361943996\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7578211044729053\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3022953.1741263247\n",
            "total_reward:2022953.1741263247\n",
            "total_cost:  998.9986360614382\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7499871433174924\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3034794.836248101\n",
            "total_reward:2034794.8362481012\n",
            "total_cost:  999.0002705945211\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7524134952479796\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3035611.0598908253\n",
            "total_reward:2035611.0598908253\n",
            "total_cost:  998.9994517822719\n",
            "total_trades:  35210\n",
            "Sharpe:  0.751224298946767\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3025457.1671319176\n",
            "total_reward:2025457.1671319176\n",
            "total_cost:  998.9986633752917\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7517964545172184\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3041055.656330986\n",
            "total_reward:2041055.656330986\n",
            "total_cost:  998.9988048372863\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7551908443838147\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3034913.564139066\n",
            "total_reward:2034913.5641390658\n",
            "total_cost:  999.0009547227162\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7522526241481273\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3031574.2165369885\n",
            "total_reward:2031574.2165369885\n",
            "total_cost:  999.0005162070407\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7514584870083608\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3041294.4768735766\n",
            "total_reward:2041294.4768735766\n",
            "total_cost:  999.000755579339\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7542438320343516\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3045522.0506182173\n",
            "total_reward:2045522.0506182173\n",
            "total_cost:  999.0007476681062\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7544841897685759\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3024340.0694520893\n",
            "total_reward:2024340.0694520893\n",
            "total_cost:  999.0004214439606\n",
            "total_trades:  35210\n",
            "Sharpe:  0.7505835079244929\n",
            "=================================\n",
            "Training time (DDPG):  6.593421852588653  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Dr49PotrfG01"
      },
      "source": [
        "### Model 5: SAC"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jr6sX5pCfkVo",
        "outputId": "c9e119c9-69eb-467c-c727-91513a60ae5b"
      },
      "source": [
        "env_train = env_setup.create_env_training(data = train,\n",
        "                                          env_class = StockEnvTrain)\n",
        "agent = DRLAgent(env = env_train)\n",
        "print(\"==============Model Training===========\")\n",
        "now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')\n",
        "sac_params_tuning={\n",
        "  'batch_size': 128,\n",
        " 'buffer_size': 100000,\n",
        "  'ent_coef':'auto_0.1',\n",
        " 'learning_rate': 0.0001,\n",
        " 'learning_starts':200,\n",
        " 'timesteps': 50000,\n",
        " 'verbose': 0}\n",
        "model_sac = agent.train_SAC(model_name = \"SAC_{}\".format(now), model_params = sac_params_tuning)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Model Training===========\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4671153.50456326\n",
            "total_reward:3671153.50456326\n",
            "total_cost:  11884.539077388286\n",
            "total_trades:  39299\n",
            "Sharpe:  1.0688190789362488\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4314328.204845832\n",
            "total_reward:3314328.2048458317\n",
            "total_cost:  8916.245771555563\n",
            "total_trades:  42205\n",
            "Sharpe:  0.9317074898013844\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4561416.860842953\n",
            "total_reward:3561416.8608429534\n",
            "total_cost:  2660.274569516846\n",
            "total_trades:  37540\n",
            "Sharpe:  1.064786900184023\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4058188.7923741587\n",
            "total_reward:3058188.7923741587\n",
            "total_cost:  2103.915152623891\n",
            "total_trades:  42124\n",
            "Sharpe:  1.0241382921606073\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3771216.4327850626\n",
            "total_reward:2771216.4327850626\n",
            "total_cost:  1586.8546648004042\n",
            "total_trades:  42300\n",
            "Sharpe:  0.9564832220143277\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3402056.0889656306\n",
            "total_reward:2402056.0889656306\n",
            "total_cost:  1690.3945055953197\n",
            "total_trades:  39154\n",
            "Sharpe:  0.886446512702707\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4406831.950821762\n",
            "total_reward:3406831.950821762\n",
            "total_cost:  1430.8199214584413\n",
            "total_trades:  39714\n",
            "Sharpe:  1.038462962253558\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3684160.6859390032\n",
            "total_reward:2684160.6859390032\n",
            "total_cost:  1150.2312432277274\n",
            "total_trades:  40289\n",
            "Sharpe:  0.9795007248995153\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:4074678.2028201707\n",
            "total_reward:3074678.2028201707\n",
            "total_cost:  1731.8181289387262\n",
            "total_trades:  38416\n",
            "Sharpe:  1.0291265508979608\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3875462.880221614\n",
            "total_reward:2875462.880221614\n",
            "total_cost:  1281.521651832104\n",
            "total_trades:  37027\n",
            "Sharpe:  0.9397242842855513\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3851483.8524622177\n",
            "total_reward:2851483.8524622177\n",
            "total_cost:  1569.0549175201854\n",
            "total_trades:  35390\n",
            "Sharpe:  0.8509112004377605\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3760485.852202145\n",
            "total_reward:2760485.852202145\n",
            "total_cost:  1742.0551374979332\n",
            "total_trades:  37119\n",
            "Sharpe:  0.8275426297559904\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37831\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37830\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37830\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37830\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37830\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37830\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "begin_total_asset:1000000\n",
            "end_total_asset:3593917.381909011\n",
            "total_reward:2593917.381909011\n",
            "total_cost:  1230.9918473982805\n",
            "total_trades:  37830\n",
            "Sharpe:  0.8649446888291069\n",
            "=================================\n",
            "Training time (SAC):  9.457694160938264  minutes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f2wZgkQXh1jE"
      },
      "source": [
        "## Trading\n",
        "Assume that we have $1,000,000 initial capital at 2019-01-01. We use the DDPG model to trade Dow jones 30 stocks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bEv5KGC8h1jE"
      },
      "source": [
        "### Set turbulence threshold\n",
        "Set the turbulence threshold to be the 99% quantile of insample turbulence data, if current turbulence index is greater than the threshold, then we assume that the current market is volatile"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "efwBi84ch1jE"
      },
      "source": [
        "data_turbulence = df[(df.date<'2019-01-01') & (df.date>='2009-01-01')]\n",
        "insample_turbulence = data_turbulence.drop_duplicates(subset=['date'])"
      ],
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VHZMBpSqh1jG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78057840-69b1-4c1d-b56c-2d5a7893be46"
      },
      "source": [
        "insample_turbulence.turbulence.describe()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    2516.000000\n",
              "mean       75.936148\n",
              "std        38.178007\n",
              "min         0.000000\n",
              "25%        58.034347\n",
              "50%        74.015083\n",
              "75%        96.948927\n",
              "max       230.256669\n",
              "Name: turbulence, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuwDPkV9h1jL"
      },
      "source": [
        "turbulence_threshold = np.quantile(insample_turbulence.turbulence.values,1)"
      ],
      "execution_count": 140,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwoz_7VSh1jO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e4b21e7-0170-461c-b138-5398ba941883"
      },
      "source": [
        "turbulence_threshold"
      ],
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "230.2566685901892"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U5mmgQF_h1jQ"
      },
      "source": [
        "### Trade\n",
        "\n",
        "DRL model needs to update periodically in order to take full advantage of the data, ideally we need to retrain our model yearly, quarterly, or monthly. We also need to tune the parameters along the way, in this notebook I only use the in-sample data from 2009-01 to 2018-12 to tune the parameters once, so there is some alpha decay here as the length of trade date extends. \n",
        "\n",
        "Numerous hyperparameters – e.g. the learning rate, the total number of samples to train on – influence the learning process and are usually determined by testing some variations."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A8pXH01Gant2"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\n",
        "                                         env_class = StockEnvTrade) "
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eLOnL5eYh1jR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb182ae2-5630-43c1-94df-a911a29ea614"
      },
      "source": [
        "env_trade, obs_trade = env_setup.create_env_trading(data = trade,\n",
        "                                         env_class = StockEnvTrade,\n",
        "                                         turbulence_threshold=250) \n",
        "\n",
        "df_account_value, df_actions = DRLAgent.DRL_prediction(model=model_sac,\n",
        "                        test_data = trade,\n",
        "                        test_env = env_trade,\n",
        "                        test_obs = obs_trade)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "previous_total_asset:1000000\n",
            "end_total_asset:1387593.0471878196\n",
            "total_reward:387593.0471878196\n",
            "total_cost:  7276.527191085817\n",
            "total trades:  6582\n",
            "Sharpe:  1.0670255446734498\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ERxw3KqLkcP4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68905bdb-4b6f-486a-e23c-b7cf5a00f456"
      },
      "source": [
        "df_account_value.shape"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(440, 2)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2yRkNguY5yvp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 199
        },
        "outputId": "38e633a2-9685-4bbf-a3df-97ad83fa3688"
      },
      "source": [
        "df_account_value.head()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>date</th>\n",
              "      <th>account_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2019-01-02</td>\n",
              "      <td>1.000000e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-01-03</td>\n",
              "      <td>9.964055e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>1.005618e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-01-07</td>\n",
              "      <td>1.007855e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-01-08</td>\n",
              "      <td>1.012859e+06</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         date  account_value\n",
              "0  2019-01-02   1.000000e+06\n",
              "1  2019-01-03   9.964055e+05\n",
              "2  2019-01-04   1.005618e+06\n",
              "3  2019-01-07   1.007855e+06\n",
              "4  2019-01-08   1.012859e+06"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W6vvNSC6h1jZ"
      },
      "source": [
        "<a id='6'></a>\n",
        "# Part 7: Backtest Our Strategy\n",
        "Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Lr2zX7ZxNyFQ"
      },
      "source": [
        "<a id='6.1'></a>\n",
        "## 7.1 BackTestStats\n",
        "pass in df_account_value, this information is stored in env class\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nzkr9yv-AdV_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1864b1a1-ffa2-4580-a6f8-c545299250ba"
      },
      "source": [
        "print(\"==============Get Backtest Results===========\")\n",
        "perf_stats_all = BackTestStats(account_value=df_account_value)\n",
        "perf_stats_all = pd.DataFrame(perf_stats_all)\n",
        "perf_stats_all.to_csv(\"./\"+config.RESULTS_DIR+\"/perf_stats_all_\"+now+'.csv')"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Backtest Results===========\n",
            "annual return:  22.980509825439487\n",
            "sharpe ratio:  1.0670255446734498\n",
            "Annual return          0.206361\n",
            "Cumulative returns     0.387593\n",
            "Annual volatility      0.193942\n",
            "Sharpe ratio           1.067026\n",
            "Calmar ratio           0.957894\n",
            "Stability              0.637709\n",
            "Max drawdown          -0.215432\n",
            "Omega ratio            1.231145\n",
            "Sortino ratio          1.504066\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.944479\n",
            "Daily value at risk   -0.023613\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9U6Suru3h1jc"
      },
      "source": [
        "<a id='6.2'></a>\n",
        "## 7.2 BackTestPlot"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lKRGftSS7pNM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "d97fa703-b5ac-4c8d-8a3f-e605917edc85"
      },
      "source": [
        "print(\"==============Compare to DJIA===========\")\n",
        "%matplotlib inline\n",
        "# S&P 500: ^GSPC\n",
        "# Dow Jones Index: ^DJI\n",
        "# NASDAQ 100: ^NDX\n",
        "BackTestPlot(df_account_value, \n",
        "             baseline_ticker = '^DJI', \n",
        "             baseline_start = '2019-01-01',\n",
        "             baseline_end = '2020-09-30')"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Compare to DJIA===========\n",
            "annual return:  22.980509825439487\n",
            "sharpe ratio:  1.0670255446734498\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (440, 7)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\"><th>Start date</th><td colspan=2>2019-01-03</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>End date</th><td colspan=2>2020-09-29</td></tr>\n",
              "    <tr style=\"text-align: right;\"><th>Total months</th><td colspan=2>20</td></tr>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Backtest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Annual return</th>\n",
              "      <td>20.688%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cumulative returns</th>\n",
              "      <td>38.759%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual volatility</th>\n",
              "      <td>19.394%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sharpe ratio</th>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Calmar ratio</th>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Stability</th>\n",
              "      <td>0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Max drawdown</th>\n",
              "      <td>-21.543%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Omega ratio</th>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Sortino ratio</th>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Skew</th>\n",
              "      <td>-0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kurtosis</th>\n",
              "      <td>8.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Tail ratio</th>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Daily value at risk</th>\n",
              "      <td>-2.361%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Alpha</th>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Beta</th>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Worst drawdown periods</th>\n",
              "      <th>Net drawdown in %</th>\n",
              "      <th>Peak date</th>\n",
              "      <th>Valley date</th>\n",
              "      <th>Recovery date</th>\n",
              "      <th>Duration</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>21.54</td>\n",
              "      <td>2020-02-14</td>\n",
              "      <td>2020-03-12</td>\n",
              "      <td>2020-06-05</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9.40</td>\n",
              "      <td>2020-06-08</td>\n",
              "      <td>2020-06-26</td>\n",
              "      <td>2020-08-07</td>\n",
              "      <td>45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.64</td>\n",
              "      <td>2020-09-02</td>\n",
              "      <td>2020-09-23</td>\n",
              "      <td>NaT</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.27</td>\n",
              "      <td>2019-07-26</td>\n",
              "      <td>2019-08-14</td>\n",
              "      <td>2019-09-05</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4.02</td>\n",
              "      <td>2019-09-13</td>\n",
              "      <td>2019-10-02</td>\n",
              "      <td>2019-11-13</td>\n",
              "      <td>44</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/pyfolio/tears.py:907: UserWarning: Passed returns do not overlap with anyinteresting times.\n",
            "  'interesting times.', UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 1008x5184 with 13 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SlLT9_5WN478"
      },
      "source": [
        "<a id='6.3'></a>\n",
        "## 7.3 Baseline Stats"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YktexHcqh1jc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7eea3ed6-5b00-46fc-be33-bc89b0ad7337"
      },
      "source": [
        "print(\"==============Get Baseline Stats===========\")\n",
        "baesline_perf_stats=BaselineStats('^DJI',\n",
        "                                  baseline_start = '2019-01-01',\n",
        "                                  baseline_end = '2020-09-30')"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "==============Get Baseline Stats===========\n",
            "\r[*********************100%***********************]  1 of 1 completed\n",
            "Shape of DataFrame:  (440, 7)\n",
            "Annual return          0.097239\n",
            "Cumulative returns     0.175892\n",
            "Annual volatility      0.287173\n",
            "Sharpe ratio           0.468543\n",
            "Calmar ratio           0.262198\n",
            "Stability              0.010261\n",
            "Max drawdown          -0.370862\n",
            "Omega ratio            1.110729\n",
            "Sortino ratio          0.641939\n",
            "Skew                        NaN\n",
            "Kurtosis                    NaN\n",
            "Tail ratio             0.807113\n",
            "Daily value at risk   -0.035647\n",
            "Alpha                  0.000000\n",
            "Beta                   1.000000\n",
            "dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A6W2J57ch1j9"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}